pytorch中文官方教程（四）——训练分类器

目录

• 1、代码的坑
• 2、相关代码
• 3、输出实例

1、代码的坑

images, labels = dataiter.next() # 错误未知 是个坑

解决办法：
用
images, labels = next(dataiter)替换images, labels = dataiter.next()

成功运行！

在网上找python迭代器的写法，也没看到.next()这样子的写法

2、相关代码

"""
我们将按顺序执行以下步骤：

使用torchvision加载并标准化 CIFAR10 训练和测试数据集
定义卷积神经网络
定义损失函数
根据训练数据训练网络
在测试数据上测试网络
"""

"""1.加载并标准化 CIFAR10"""

import torch
import torchvision
import torchvision.transforms as transforms

# TorchVision 数据集的输出是[0, 1]范围的PILImage图像。 我们将它们转换为归一化范围[-1, 1]的张量。 .. 注意：

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='.\\data', train=True,
                                        download=True, transform=transform)
# trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
#                                           shuffle=True, num_workers=2) # 报错原因：在linux系统中可以使用多个子进程加载数据，而在windows系统中不能。所以在windows中要将DataLoader中的num_workers设置为0或者采用默认为0的设置
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=0)

testset = torchvision.datasets.CIFAR10(root='.\\data', train=False,
                                       download=True, transform=transform)
# testloader = torch.utils.data.DataLoader(testset, batch_size=4,
#                                          shuffle=False, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=0)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')


import matplotlib.pyplot as plt
import numpy as np

# functions to show an image

def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()

# get some random training images
dataiter = iter(trainloader)
# images, labels = dataiter.next()  # 错误未知 是个坑
images, labels = next(dataiter)

# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))

"""2.定义卷积神经网络"""

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

"""3.定义损失函数和优化器"""
import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)




"""4.训练网络"""
is_train = True
if is_train:
    for epoch in range(10):  # loop over the dataset multiple times

        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            if i % 2000 == 1999:    # print every 2000 mini-batches
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0

    print('Finished Training')


    PATH = './cifar_net.pth'
    torch.save(net.state_dict(), PATH)


"""5.根据测试数据测试网络"""
dataiter = iter(testloader)
# images, labels = dataiter.next()
images, labels = next(dataiter)

# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))


net = Net()
PATH = './cifar_net.pth'
net.load_state_dict(torch.load(PATH))
outputs = net(images)
_, predicted = torch.max(outputs, 1)
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
                              for j in range(4)))


# 让我们看一下网络在整个数据集上的表现。
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

# # 嗯，哪些类的表现良好，哪些类的表现不佳：
# class_correct = list(0. for i in range(10))
# class_total = list(0. for i in range(10))
# with torch.no_grad():
#     for data in testloader:
#         images, labels = data
#         outputs = net(images)
#         _, predicted = torch.max(outputs, 1)
#         c = (predicted == labels).squeeze()
#         for i in range(4):
#             label = labels[i]
#             class_correct[label] += c[i].item()
#             class_total[label] += 1
#
# for i in range(10):
#     print('Accuracy of %5s : %2d %%' % (
#         classes[i], 100 * class_correct[i] / class_total[i]))


# """
# 在 GPU 上进行训练
# """
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#
# # Assuming that we are on a CUDA machine, this should print a CUDA device:
#
# print(device)
# # 然后，这些方法将递归遍历所有模块，并将其参数和缓冲区转换为 CUDA 张量：
# net.to(device)
# # 请记住，您还必须将每一步的输入和目标也发送到 GPU：
# inputs, labels = data[0].to(device), data[1].to(device)

3、输出实例

F:\GoogLeNet-PyTorch-main\envs\Scripts\python.exe F:/my_pytorch/pytorch_official/4_训练分类器.py
Files already downloaded and verified
Files already downloaded and verified
deer dog bird bird
[1, 2000] loss: 2.210
[1, 4000] loss: 1.897
[1, 6000] loss: 1.708
[1, 8000] loss: 1.609
[1, 10000] loss: 1.531
[1, 12000] loss: 1.463
[2, 2000] loss: 1.414
[2, 4000] loss: 1.392
[2, 6000] loss: 1.365
[2, 8000] loss: 1.321
[2, 10000] loss: 1.315
[2, 12000] loss: 1.283
[3, 2000] loss: 1.225
[3, 4000] loss: 1.208
[3, 6000] loss: 1.238
[3, 8000] loss: 1.207
[3, 10000] loss: 1.198
[3, 12000] loss: 1.214
[4, 2000] loss: 1.118
[4, 4000] loss: 1.128
[4, 6000] loss: 1.127
[4, 8000] loss: 1.113
[4, 10000] loss: 1.109
[4, 12000] loss: 1.149
[5, 2000] loss: 1.035
[5, 4000] loss: 1.050
[5, 6000] loss: 1.077
[5, 8000] loss: 1.037
[5, 10000] loss: 1.041
[5, 12000] loss: 1.058
[6, 2000] loss: 0.981
[6, 4000] loss: 1.002
[6, 6000] loss: 1.010
[6, 8000] loss: 0.993
[6, 10000] loss: 1.011
[6, 12000] loss: 0.984
[7, 2000] loss: 0.900
[7, 4000] loss: 0.966
[7, 6000] loss: 0.944
[7, 8000] loss: 0.952
[7, 10000] loss: 0.954
[7, 12000] loss: 0.962
[8, 2000] loss: 0.874
[8, 4000] loss: 0.901
[8, 6000] loss: 0.891
[8, 8000] loss: 0.899
[8, 10000] loss: 0.913
[8, 12000] loss: 0.927
[9, 2000] loss: 0.817
[9, 4000] loss: 0.845
[9, 6000] loss: 0.874
[9, 8000] loss: 0.874
[9, 10000] loss: 0.891
[9, 12000] loss: 0.903
[10, 2000] loss: 0.795
[10, 4000] loss: 0.813
[10, 6000] loss: 0.851
[10, 8000] loss: 0.854
[10, 10000] loss: 0.852
[10, 12000] loss: 0.866
Finished Training
GroundTruth: cat ship ship plane
Predicted: dog plane ship plane
Accuracy of the network on the 10000 test images: 62 %

进程已结束,退出代码0